A quality inspection and tracing method and system for smart factories

Abstract

The present invention is applicable to the field of intelligent detection technology, and provides a quality inspection and tracing method and system for intelligent factories, the method comprising the following steps: establishing a control strategy for an equipment cluster based on experimental data in production; when a product is being processed, acquiring multiple process images about the product according to the control strategy; sorting the acquired process images through a data cloud platform to obtain an image association set corresponding to the production process; detecting the process images included in the image association set, and tracing the process in the production line according to the detection results, which improves the overall efficiency of the production line without affecting product quality inspection, and the collected process images can correspond to the production process, which also facilitates the rapid processing of quality inspection and tracing.

Description

Technical Field

[0001] The present invention relates to the field of intelligent detection technology, and in particular to a quality inspection and tracing method and system for an intelligent factory. Background Art

[0002] Smart factories use a variety of modern technologies to automate factory office, management and production, thereby improving work efficiency and achieving safe production. Smart factories also achieve mutual coordination and cooperation between people and machines.

[0003] For factory production, product quality control is a very important link, especially for some parts with high precision requirements, which need to undergo quality inspection before they can be shipped out of the factory for use. Traditional quality inspection work is done manually with tools. With the development of technology, computer vision quality inspection is becoming more and more widely used. Using machines to perform product quality inspection can greatly improve the overall production efficiency of the assembly line.

[0004] Generally speaking, intelligent machine inspection is placed at the end of the production line to control the quality of parts. For complex processing, it is necessary to control the parts to constantly change their angles and directions through equipment to collect multiple images. The multiple images are used to analyze whether the specifications of the parts' size, shape, color, hole position, etc. meet the requirements, and determine whether there is something wrong with a certain process in the processing based on the analysis results. In this process, because images of various aspects of the parts need to be collected, the time consumed is undoubtedly longer than that of a single project. At the same time, it also means that the entire production line has more pauses during operation. It is also very necessary to reduce this time loss. Therefore, a quality inspection and traceability method and system for smart factories are proposed. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a quality inspection and traceability method and system for a smart factory to solve the problems existing in the above-mentioned background technology.

[0006] The present invention is implemented as follows: a quality inspection and tracing method for a smart factory, the method comprising the following steps:

[0007] A control strategy for a device cluster is established based on experimental data in production. The device cluster includes multiple image acquisition devices, which correspond to the production processes and acquire images at specific angles by adjusting the product.

[0008] When a product is being processed, a plurality of process images of the product are acquired according to a control strategy, wherein the process images are captured by an image acquisition device;

[0009] The acquired process images are sorted through the data cloud platform to obtain an image association set corresponding to the production process;

[0010] The process images included in the image association set are inspected, and the process traceability in the production line is performed based on the inspection results.

[0011] As a further solution of the present invention: the step of establishing a control strategy for the equipment cluster based on experimental data in production specifically includes:

[0012] When performing an experimental processing, using an image acquisition device to obtain a plurality of image sets of the experimental product;

[0013] Arrange the image sets according to the order of production and processing, the Nth image set includes reference images taken at the corresponding process and additional images that are the same as all the images in the N-1th image set, and the images contained in the N-1th image set are all acquired by the image acquisition device corresponding to this process;

[0014] The arranged image set is differentially analyzed and a control strategy is generated to determine the interference between various processes in the production line.

[0015] As a further solution of the present invention: the step of performing differential analysis on the arranged image set and generating a control strategy specifically includes:

[0016] Compare and analyze the additional image in the Nth image set with the corresponding image in the N-1th image set, where the Nth image set includes the reference image and the N-1 additional images;

[0017] When the image comparisons are identical, the reference image in the Nth image set and the reference image in the N-1th image set are marked;

[0018] When there are differences in the image comparison, the additional images and reference images that are different in the Nth image set are marked;

[0019] A control strategy is generated according to the marked results.

[0020] As a further solution of the present invention: the step of generating a control strategy according to the marked result specifically includes:

[0021] Traverse the marking status of all images from the first image set to the Nth image set;

[0022] Generate a device control instruction according to the marked images in all the image sets, so as to enable the image acquisition device corresponding to the image set to complete the acquisition of the marked images;

[0023] The control strategy is obtained by integrating the device control instructions corresponding to all image acquisition devices.

[0024] As a further solution of the present invention: the step of detecting the process images included in the image association set and performing traceability processing on the process in the production line according to the detection results specifically includes:

[0025] Arranging the process images in the image association set according to the production process to indicate the change process of the product on the production line;

[0026] Grayscale the arranged process images;

[0027] Comparing the grayscaled process image with a standard image, wherein the standard image is a preset image of standard specifications for the product;

[0028] When there is an abnormality in the image comparison, the image acquisition device is traced back according to the abnormal process image and the corresponding production process is determined.

[0029] As a further solution of the present invention: the step of comparing the grayscale processed process image with the standard image specifically includes:

[0030] Calibrate the contour lines of the grayscaled process image and the standard image according to the feature areas;

[0031] Select the specified inflection points in the contour lines of the two images as reference points;

[0032] Overlap the contours of the two images with reference to the reference point, and calculate the difference between the two contours;

[0033] When the difference is less than the preset value, the comparison processing of the process image passes.

[0034] Another object of the present invention is to provide a quality inspection and tracing system for a smart factory, the system comprising:

[0035] A strategy building module, which establishes a control strategy for a device cluster based on experimental data in production. The device cluster includes multiple image acquisition devices, which correspond to the production processes and acquire images at specific angles by adjusting the product.

[0036] An image acquisition module, used to acquire a plurality of process images of the product according to a control strategy when the product is being processed, wherein the process images are captured by an image acquisition device;

[0037] An image association module is used to organize the acquired process images through the data cloud platform to obtain an image association set corresponding to the production process;

[0038] The detection and tracing module is used to detect the process images contained in the image association set, and to trace the processes in the production line based on the detection results.

[0039] As a further solution of the present invention: the strategy building module includes:

[0040] An image acquisition unit, used to acquire a plurality of image sets about the experimental product by using an image acquisition device when performing an experimental processing process;

[0041] An image arrangement unit, used to arrange the image sets according to the order of production and processing, wherein the Nth image set includes a reference image taken at a corresponding process and additional images identical to all images in the N-1th image set, and the images contained in the N-1th image set are all acquired by an image acquisition device corresponding to this process;

[0042] The differential analysis unit is used to perform differential analysis on the arranged image set and generate a control strategy for determining the interference between various processes in the production line.

[0043] As a further solution of the present invention: the differential analysis unit comprises:

[0044] An image repeatability comparison subunit, for comparing and analyzing the additional image in the Nth image set with the corresponding image in the N-1th image set, wherein the Nth image set includes the reference image and the N-1 additional images;

[0045] A first marking subunit, used for marking the reference image in the Nth image set and the reference image in the N-1th image set when the images are compared to be identical;

[0046] A second marking subunit is used to mark the additional images and the reference images that are different in the Nth image set when there are differences in the image comparison;

[0047] The image marking integration subunit is used to generate a control strategy according to the marking result.

[0048] As a further solution of the present invention: the detection and tracing module includes:

[0049] An image arranging unit, used for arranging the process images in the image association set according to the production process, so as to indicate the change process of the product on the production line;

[0050] An image preprocessing unit, used for graying the arranged process images;

[0051] A comparison unit, used for comparing the grayscaled process image with a standard image, wherein the standard image is a preset image of standard specifications for the product;

[0052] The tracing unit is used to trace the image acquisition device and determine the corresponding production process based on the abnormal process image when there is an abnormality in the image comparison.

[0053] Compared with the prior art, the beneficial effect of the present invention is as follows: in the production process of the intelligent factory, the present invention refers to the established control strategy to control the image acquisition equipment to obtain the process images of the product after each process, and then performs product quality inspection and quality inspection traceability processing based on these process images. The characteristic is that for the complex processing process, the conventional quality inspection needs to collect images of various features of the product at one workstation, but the present invention collects them separately according to the production process, and the collected images can correspond to the process, that is, the image collection time in the quality inspection process is separated. For the entire production line, the time that the product is paused during operation is undoubtedly shorter. On the basis of not affecting the product quality inspection, the overall efficiency of the production line is improved, and the collected process images can correspond to the production process, which also helps to quickly process the quality inspection traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0055] Figure 1 The figure is a flow chart of the quality inspection and traceability method used in smart factories.

[0056] Figure 2 A flowchart of the control strategy for establishing a cluster of equipment based on experimental data in production in the quality inspection and traceability method for smart factories.

[0057] Figure 3 A flowchart is provided for performing differential analysis on the arranged image sets and generating control strategies in the quality inspection and traceability method for smart factories.

[0058] Figure 4 The invention provides a flow chart for generating a control strategy according to the result of the marking in a quality inspection and traceability method for a smart factory.

[0059] Figure 5 The present invention is a flow chart for detecting process images contained in an image association set in a quality inspection and tracing method for a smart factory, and tracing processes in a production line based on the detection results.

[0060] Figure 6 This is a flowchart for comparing the grayscaled process image with the standard image.

[0061] Figure 7This is a structural block diagram of the quality inspection and traceability system used in smart factories.

[0062] Figure 8 This is a structural block diagram of the strategy building module in the quality inspection and traceability system for smart factories.

[0063] Fig. 9 This is a structural block diagram of the composition of the differentiation analysis unit in the strategy building module.

[0064] Fig.10 This is a structural block diagram of the detection and traceability module in the quality inspection and traceability system of the smart factory. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0066] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0067] like Figure 1 As shown, an embodiment of the present invention provides a quality inspection and tracing method for a smart factory, the method comprising the following steps:

[0068] Step S100, establishing a control strategy for a device cluster based on experimental data in production, wherein the device cluster includes a plurality of image acquisition devices, wherein the image acquisition devices correspond to production processes, and the image acquisition devices acquire images at specific angles by adjusting products;

[0069] The experimental processing process is not an actual production process. When the present invention is applied, it needs to be entered into the system of the smart factory. In the actual production process, not every process will change the appearance of the product, that is, some processes in the production line have no effect on quality inspection, so the images of the products processed by these processes do not need to be obtained. In addition, some processes in the production line will change the previous processes, that is, a certain feature of the product will still change in a later process after being changed in one process. At this time, it is obviously inapplicable to use the image of the previous process, and the processing process of the production line is fixed. The above method can determine which image acquisition devices need to be started, and can also determine which aspects of the product images the image acquisition devices can capture.

[0070] Step S200, when a product is being processed, a plurality of process images of the product are acquired according to a control strategy, wherein the process images are acquired by taking pictures of an image acquisition device;

[0071] There is a one-to-one correspondence between the process images and the features of the product that need to be inspected. The determination of the production process also means that the quality inspection goals of the product are also clear and unchanging.

[0072] Step S300, sorting the acquired process images through the data cloud platform to obtain an image association set corresponding to the production process;

[0073] The image association set is composed of the process images, and the image association set can represent the change process of individual products.

[0074] Step S400: Detect the process images included in the image association set, and perform tracing processing of the processes in the production line according to the detection results.

[0075] The process images in the image association set correspond to the image acquisition devices, that is, if there is an abnormality in the process image, it can be determined which image acquisition device uploaded it, and then the problem in the processing process can be traced back.

[0076] In an embodiment of the present invention, during the production process of a smart factory, the present invention controls the image acquisition device by referring to the established control strategy to obtain process images of the product after each process, and then performs product quality inspection and quality inspection traceability processing based on these process images. The characteristic is that for a complex processing process, conventional quality inspection requires the collection of images of various features of the product at one workstation, but the present invention collects images separately according to the production process, and the collected images can correspond to the process, that is, the image collection time during the quality inspection process is separated. For the entire production line, the time that the product is paused during operation is undoubtedly shorter. On the basis of not affecting the product quality inspection, the overall efficiency of the production line is improved, and the collected process images can correspond to the production process, which also helps to quickly process the quality inspection traceability.

[0077] It should be noted that the technical solutions of the present invention are all completed by computer equipment, and the connection between steps can be completed by AI. For example, the organization and detection of process images are performed by AI to organize the process images according to the production process, and then the content of the process images is identified and detected; the image acquisition device is generally an industrial camera, which is also used to detect the readability of the captured process images.

[0078] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of establishing a control strategy for a device cluster based on experimental data in production specifically includes:

[0079] Step S101, when performing an experimental processing, using an image acquisition device to obtain a plurality of image sets of the experimental product;

[0080] Step S102, arranging the image sets according to the order of production and processing, the Nth image set includes reference images taken at the corresponding process and additional images that are the same as all the images in the N-1th image set, and the images contained in the N-1th image set are all acquired by the image acquisition device corresponding to this process;

[0081] Step S103, performing differential analysis on the arranged image set and generating a control strategy for determining the interference between various processes in the production line.

[0082] The embodiment of the present invention illustrates the generation process of the control strategy. In step S101, the image set is acquired by repeated incremental acquisition. For example, the first image acquisition device acquires image 1 about a certain size feature of the product, and the second image acquisition device not only needs to acquire image 2 that it needs to acquire, but also needs to acquire image 1+ according to the first image acquisition device, wherein the content of image 1+ is the same as that of image 1, and the two are not derived from the same image acquisition device. Image 1 and image 2 are the reference images, and image 1+ is the additional image. A large number of images are used for analysis to facilitate obtaining an accurate control strategy.

[0083] like Figure 3 As shown, as a preferred embodiment of the present invention, the step of performing differential analysis on the arranged image set and generating a control strategy specifically includes:

[0084] Step S113, comparing and analyzing the additional image in the Nth image set with the corresponding image in the N-1th image set, where the Nth image set includes the reference image and the N-1 additional images;

[0085] Step S123, when the images are compared to be identical, marking the reference image in the Nth image set and the reference image in the N-1th image set;

[0086] Step S133, when there are differences in the image comparison, marking the additional images and reference images that are different in the Nth image set comparison;

[0087] Step S143, generating a control strategy according to the marking result.

[0088] In the embodiment of the present invention, for the sake of convenience, based on the previous embodiment, the value of N is taken as 3, the first image set includes image 1, the second image set includes image 2 and image 1+, and the third image set includes image 3, image 2+ and image 1++, where image 1, image 2 and image 3 are reference images, and image 1+, Figure 2+ and image 1++ are additional images. First, image 1+ is compared with image 1 to determine whether the second process affects the characteristics corresponding to the first process. When the two are the same, image 1 is of reference value. When the two are different, image 1+ will replace image 1, that is, the first image set has no reference value. Then image 1++ is compared with image 1+ and image 2+ is compared with image 2 to determine the impact of the third process on the previous process. The images marked by the above method are images with reference significance in the product production process.

[0089] like Figure 4 As shown, as a preferred embodiment of the present invention, the step of generating a control strategy according to the marked result specifically includes:

[0090] Step S1431, traversing the marking conditions of all images from the first image set to the Nth image set;

[0091] Step S1432, generating a device control instruction according to the marked images in all the image sets, so as to enable the image acquisition device corresponding to the image set to complete the acquisition of the marked images;

[0092] Step S1433, integrating the device control instructions corresponding to all image acquisition devices to obtain the control strategy.

[0093] In the embodiment of the present invention, for the sake of convenience of explanation, this embodiment still refers to the examples in the previous text. For example, when image 3, image 2+ and image 1++ in the third image set are all marked, then in actual production, the device control instruction is to control the image acquisition device corresponding to the third image set to acquire three different images, and the image acquisition device of the previous process does not need to acquire images because all features of the product will be acquired at this step. When image 1, image 3 and image 2+ are marked, the device control instruction is to control the first image acquisition device to acquire images of the first process, and the second image acquisition device does not acquire images. The third image acquisition device not only needs to acquire image 3 but also needs to replace the second image acquisition device to perform acquisition work.

[0094] like Figure 5 As shown, as a preferred embodiment of the present invention, the step of detecting the process images included in the image association set and performing traceability processing on the process in the production line according to the detection results specifically includes:

[0095] Step S401, arranging the process images in the image association set according to the production process to indicate the change process of the product on the production line;

[0096] Step S402, graying the arranged process images;

[0097] Step S403, comparing the grayscaled process image with a standard image, where the standard image is a preset image of standard specifications for the product;

[0098] Step S404: When there is an abnormality in the image comparison, the image acquisition device is traced back according to the abnormal process image and the corresponding production process is determined.

[0099] In the embodiment of the present invention, the process image may include one or more images, but the redundant images do not correspond to the production process to which the process image originally corresponds, so they need to be arranged. Before comparing the images, they need to be grayed out so that the features contained in the process image can be more obvious. This example uses the weighted average method to gray out the process image, and assigns different weights to R, G, and B according to the importance of the three primary colors or other indicators, namely:

[0100] R=G=B=(Wr*R+Wg*G+Wb*B) / (Wr+Wg+Wb)

[0101] Among them, Wr, Wg, and Wb are the weights of R, G, and B respectively.

[0102] When Wr, Wg, and Wb take different values, the weighted average method will form different grayscale images. Since the human eye is most sensitive to green, second to red, and least sensitive to blue, making Wg>Wr>Wb will produce a more reasonable grayscale image. Experiments and theoretical derivations have proved that when Wr=0.299, Wg=0.587, and Wb=0.114, the grayscale image obtained is the most reasonable. At this time, the values ​​of R, G, and B are the grayscale values ​​of the pixel. Of course, grayscale is not limited to the weighted average method, and the maximum value method and the average value method can also be used.

[0103] like Figure 6 As shown, as a preferred embodiment of the present invention, the step of comparing the grayscale processed process image with the standard image specifically includes:

[0104] Step S413, calibrating the contour lines of the grayscaled process image and the standard image according to the feature areas;

[0105] Step S423, selecting a designated inflection point in the contour lines of the two images as a reference point;

[0106] Step S433, overlapping the contour lines of the two images with reference to the reference point, and calculating the difference between the two contour lines;

[0107] Step S443: when the difference is less than the preset value, the comparison of the process images is passed.

[0108] In the embodiment of the present invention, the process image not only contains the product itself, but also carries other objects. In order to avoid the impact during comparison, it is necessary to select the outline of the product, that is, the contour line. The selected reference point can better adjust the two contour lines to overlap. Of course, there will inevitably be errors in the actual product processing, that is, the two contour lines are not completely overlapped, and the difference corresponds to the allowable range of the processing error. When the difference is within the allowable range, the product inspection passes.

[0109] like Figure 7 As shown, an embodiment of the present invention further provides a quality inspection and tracing system for a smart factory, the system comprising:

[0110] A strategy building module 100 is used to establish a control strategy for a device cluster based on experimental data in production. The device cluster includes a plurality of image acquisition devices, and the image acquisition devices correspond to production processes. The image acquisition devices acquire images at specific angles by adjusting products.

[0111] An image acquisition module 200 is used to acquire a plurality of process images of the product according to a control strategy when the product is being processed, wherein the process images are captured by an image acquisition device;

[0112] The image association module 300 is used to organize the acquired process images through the data cloud platform to obtain an image association set corresponding to the production process;

[0113] The detection and tracing module 400 is used to detect the process images included in the image association set, and perform tracing processing of the processes in the production line according to the detection results.

[0114] In an embodiment of the present invention, during the production process of a smart factory, the present invention controls the image acquisition device by referring to the established control strategy to obtain process images of the product after each process, and then performs product quality inspection and quality inspection traceability processing based on these process images. The characteristic is that for a complex processing process, conventional quality inspection requires the collection of images of various features of the product at one workstation, but the present invention collects images separately according to the production process, and the collected images can correspond to the process, that is, the image collection time during the quality inspection process is separated. For the entire production line, the time that the product is paused during operation is undoubtedly shorter. On the basis of not affecting the product quality inspection, the overall efficiency of the production line is improved, and the collected process images can correspond to the production process, which also helps to quickly process the quality inspection traceability.

[0115] like Figure 8As shown, as a preferred embodiment of the present invention, the strategy building module 100 includes:

[0116] An image acquisition unit 101 is used to acquire a plurality of image sets of the experimental product by using an image acquisition device during an experimental processing process;

[0117] An image arrangement unit 102 is used to arrange the image sets according to the order of production and processing, wherein the Nth image set includes a reference image taken at a corresponding process and additional images identical to all images in the N-1th image set, and the images contained in the N-1th image set are all acquired by an image acquisition device corresponding to this process;

[0118] The differential analysis unit 103 is used to perform differential analysis on the arranged image set and generate a control strategy for determining the interference between various processes in the production line.

[0119] like Fig. 9 As shown, as a preferred embodiment of the present invention, the difference analysis unit 103 includes:

[0120] An image repeatability comparison subunit 113, used for comparing and analyzing the additional image in the Nth image set with the corresponding image in the N-1th image set, where the Nth image set includes the reference image and the N-1 additional images;

[0121] A first marking subunit 123, used for marking the reference image in the Nth image set and the reference image in the N-1th image set when the images are compared to be identical;

[0122] The second marking subunit 133 is used to mark the additional images and the reference images that are different in the Nth image set when there are differences in the image comparison;

[0123] The image marking integration subunit 143 is used to generate a control strategy according to the marking result.

[0124] like Fig.10 As shown, as a preferred embodiment of the present invention, the detection and tracing module 400 includes:

[0125] An image arranging unit 401, used to arrange the process images in the image association set according to the production process, so as to indicate the change process of the product on the production line;

[0126] An image preprocessing unit 402, used for graying the arranged process images;

[0127] A comparison unit 403 is used to compare the grayscaled process image with a standard image, where the standard image is a preset image of standard specifications for the product;

[0128] The tracing unit 404 is used to trace the image acquisition device and determine the corresponding production process according to the abnormal process image when there is an abnormality in the image comparison.

[0129] The above only describes in detail the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0130] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0131] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0132] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A quality inspection and tracing method for a smart factory, characterized in that: The method comprises the following steps: A control strategy for a device cluster is established based on experimental data in production. The device cluster includes multiple image acquisition devices, which correspond to the production processes and acquire images at specific angles by adjusting the product. When a product is being processed, a plurality of process images of the product are acquired according to a control strategy, wherein the process images are captured by an image acquisition device; The acquired process images are sorted through the data cloud platform to obtain an image association set corresponding to the production process; Detect the process images contained in the image association set, and perform traceability processing on the production line process based on the detection results; The step of establishing a control strategy for the equipment cluster based on experimental data in production specifically includes: When performing an experimental processing, using an image acquisition device to obtain a plurality of image sets of the experimental product; Arrange the image sets according to the order of production and processing, the Nth image set includes reference images taken at the corresponding process and additional images that are the same as all the images in the N-1th image set, and the images contained in the N-1th image set are all acquired by the image acquisition device corresponding to this process; Perform differential analysis on the arranged image set and generate control strategies to determine the interference between various processes in the production line; The step of performing differential analysis on the arranged image set and generating a control strategy specifically includes: Compare and analyze the additional image in the Nth image set with the corresponding image in the N-1th image set, where the Nth image set includes the reference image and the N-1 additional images; When the image comparisons are identical, the reference image in the Nth image set and the reference image in the N-1th image set are marked; When there are differences in the image comparison, the additional images and reference images that are different in the Nth image set are marked; generating a control strategy according to the marked results; The step of generating a control strategy according to the marked result specifically includes: Traverse the marking status of all images from the first image set to the Nth image set; Generate a device control instruction according to the marked images in all the image sets, so as to enable the image acquisition device corresponding to the image set to complete the acquisition of the marked images; The control strategy is obtained by integrating the device control instructions corresponding to all image acquisition devices.

2. The quality inspection and tracing method for a smart factory according to claim 1, characterized in that: The step of detecting the process images included in the image association set and performing traceability processing on the process in the production line according to the detection results specifically includes: Arranging the process images in the image association set according to the production process to indicate the change process of the product on the production line; Grayscale the arranged process images; Comparing the grayscaled process image with a standard image, wherein the standard image is a preset image of standard specifications for the product; When there is an abnormality in the image comparison, the image acquisition device is traced back according to the abnormal process image and the corresponding production process is determined.

3. The quality inspection and tracing method for a smart factory according to claim 2 is characterized in that: The step of comparing the grayscale processed process image with the standard image specifically includes: Calibrate the contour lines of the grayscaled process image and the standard image according to the feature areas; Select the specified inflection points in the contour lines of the two images as reference points; Overlap the contours of the two images with reference to the reference point, and calculate the difference between the two contours; When the difference is less than the preset value, the comparison processing of the process image passes.

4. A quality inspection and traceability system for smart factories, characterized in that: The system comprises: A strategy building module, which establishes a control strategy for a device cluster based on experimental data in production. The device cluster includes multiple image acquisition devices, which correspond to the production processes and acquire images at specific angles by adjusting the product. An image acquisition module, used to acquire a plurality of process images of the product according to a control strategy when the product is being processed, wherein the process images are captured by an image acquisition device; An image association module is used to organize the acquired process images through the data cloud platform to obtain an image association set corresponding to the production process; The detection and tracing module is used to detect the process images contained in the image association set and perform tracing processing on the process in the production line based on the detection results; The strategy building module includes: An image acquisition unit, used to acquire a plurality of image sets about the experimental product by using an image acquisition device when performing an experimental processing process; An image arrangement unit, used to arrange the image sets according to the order of production and processing, wherein the Nth image set includes a reference image taken at a corresponding process and additional images identical to all images in the N-1th image set, and the images contained in the N-1th image set are all acquired by an image acquisition device corresponding to this process; A differential analysis unit, used for differentially analyzing the arranged image set and generating a control strategy for determining interference between various processes in the production line; The differential analysis unit comprises: An image repeatability comparison subunit, for comparing and analyzing the additional image in the Nth image set with the corresponding image in the N-1th image set, wherein the Nth image set includes the reference image and the N-1 additional images; A first marking subunit, used for marking the reference image in the Nth image set and the reference image in the N-1th image set when the images are compared to be identical; A second marking subunit is used to mark the additional images and the reference images that are different in the Nth image set when there are differences in the image comparison; An image marking integration subunit, used for generating a control strategy according to the marking result; Generating a control strategy according to the marked result specifically includes: Traverse the marking status of all images from the first image set to the Nth image set; Generate a device control instruction according to the marked images in all the image sets, so as to enable the image acquisition device corresponding to the image set to complete the acquisition of the marked images; The control strategy is obtained by integrating the device control instructions corresponding to all image acquisition devices.

5. The quality inspection and traceability system for smart factories according to claim 4 is characterized in that: The detection and tracing module includes: An image arranging unit, used for arranging the process images in the image association set according to the production process, so as to indicate the change process of the product on the production line; An image preprocessing unit, used for graying the arranged process images; A comparison unit, used for comparing the grayscaled process image with a standard image, wherein the standard image is a preset image of standard specifications for the product; The tracing unit is used to trace the image acquisition device and determine the corresponding production process based on the abnormal process image when there is an abnormality in the image comparison.

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